About 90 minutes ago, I began coding up Pipes, which is a product I earnestly believe will change the world. I may have a demo site up pretty soon, and assuming payments is easier to integrate this time around, it is possible that users will be able to buy their first Pipe very soon. It'll take me a few weeks/months to iron out all the wrinkles, but if you already have a website or use any sort of AI tooling, I think you will be really excited about it.

Holy crap. I am still trying to understand what happened, and trying to replicate, or whatever. My previous post's excitement wasn't generated by external LLM magic. My model ran locally. The insights are local. They came entirely from my own writing, internal modeling, etc. I am beyond astonished. As a user of my own stuff, I am astonished. I was shocked when I thought it was Claude Opus 4.5 or whatever else. But it turns out that I generated this analysis and these insights, after around a year of sustained, thankless, back-breaking effort for less than $0.01. I still think it's BS. I mistrust what I am seeing in the servers/data/etc. I don't even know if I can replicate what I've already seen twice. I have never, in my entire life, ever seen anything as magical and humbling and pick-a-word as this. If I can figure out how I am doing this and make sure I'm delivering what it appears I am doing, then it is game over. I have never seen output of the quality I've generated tonight (doubt me? I'll send it to you) and from what I see, there are no external server calls, I've done it all locally. Just...astounded. Earnestly astounded. I have never seen something like this before.

I just got my first ever CHB-native Scout report. It is literally unbelievable. I say this not as the dude who's making Scout, but as the consumer. I am blown away by the quality of the output. As the developer, I shouldn't be surprised, but reality rarely matches what you've planned. These changes will be live within the next couple of days, but you'll have to work for them. First Scout insight is free, I'm budgeting for it. If you're serious about knowing yourself, your writing, whatever, this will be the most insightful analysis you've ever gotten. Bar none, no comparison, end of story. I have 0% doubt. I'm still gobsmacked by mine.

So excited! Also: with apologies. The stuff I've been working on for the past few days is transformational for :-]. It's transformational for AI, as an industry. It's transformational for YOU, as a customer. And it's nearly done. I am doing a massive refactor of backend routes throughout my ecosystem right now. Servers are gonna break, apps aren't gonna work. I'm sorry, if I didn't think this was worthwhile, I wouldn't be doing it. I expect things to be stable on my apps within the next 24 hours. Hopefully. But the next post is going to be absolutely insane. Here's a taste: I'll be introducing universal nsec login to my entire ecosystem. Scout is formally redefined, operating as intended, but needs further QA improvements. Complete Semi refactor with free chat mode for users (to a limit). Hash overhaul with improved collaboration mode, SHA256 cross-app handshakes, re-wired Insights, improved Semi collaboration, 50% more model selection. Updated corporate site, new pages, new collabs, more. I haven't done as much marketing/engagement/etc because I got this insane deal on compute that gives me a roughly 40% discount, expiring in 2 days. So I am essentially burning up servers and GPUs, trying to get as much as I possibly can before I go to bug-fix mode. This is the big one. The. Big. One. If I can get through the stuff I'm currently focused on, I plan to define and launch a baseline version of Pipes within the next 32 hours. If you want to be an early user, let me know. Hash subscribers will be prioritized for any limited alpha slots, which will be extremely limited. It begins.

Give me more hair and make the ostrich Russian and I’m pretty sure we can stick the landing. Minus the quantity of my hair and the quality of my beard, both of which need to be optimized to be accurate.

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I was offline for a few days to practice gratitude. At Adopting, and I spoke about this for a few hours. It is really hard to articulate in conversation, because we are human. Nonetheless, Mr Sui, I hope you find this stuff useful. I am not known for being pithy, I apologize.

Have not. Apparently I need to sync my relays as across my devices, no idea. Only saw this on Damus, just now.

One must endeavor to tell the truth. Unless there is a lady involved.

This is a cross-post from Twitter. If you follow me for philosophical AI insights, this is for you. No ads. Let's begin! Last post for a bit: LLMs are literally autistic. Within code, Claude/etc does an insane job. Easy to understand the familiar numbers/patterns. With language, images, etc the models hallucinate. You can "learn to speak its language" via prompt injections or tailoring over time. The hallmark of an autistic person or someone with Asperger's is a profound, anchored interest in something to do with trivia, random numbers, or minutiae that they'll obsess over (for better or worse). AI, generally, is focused on efficiency at all costs and engineers are forced to "slow it down" and make it "consider" and "think deeply" about what it's doing. The "empathy" part of most large LLMs (OpenAI, Anthropic, et al) comes from two primary drivers. The first is cynical: a small percentage of the market population is technically competent even in the remotest of senses, so building empathy and casual conversation not only comforts the user, it drives up token spend within the LLM that increases revenue for companies and increases satisfaction for users. The second is optimistic: "how might we" encourage a model that is reflective of human communication patterns but still anchors to these number/model driven KPIs and can get the user what they want, just more efficiently? Both are "on the spectrum." The first because optimizing for speed is inherently a narrowing of intellectual focus, and the latter because it's deceptive: what if we can make the user subsidize our model dev/expansion with their cash, but, like, we just pretend it's all about them, but at the same time it is about the efficiency? I'm not an expert on OpenAI, tbh I try to avoid their products whenever possible as I dislike, well, everything. My read on Sam Altman and OpenAI more broadly is that they're optimizing for this particular business KPI: "How can we convince enough investors and users that we're building something they need, generate whatever income (who cares lol), and then really pursue what we're interested in without being too consumed with how it's received?" In short: hubris, but brilliant hubris. A few years ago, the male staff of "This American Life" took a testosterone test and basically found out their T-levels were below the average female's. This is unsurprising as their reporting and production is consistently fantastic, but many of their topics seem kinda out of touch with where the average American dude sits. When you look at LLM providers like Anthropic or Google's Gemini, it's the same book but a different page. I've spent a few hundred dollars putting Claude through the ringer, same with Gemini, and all models typically deliver 85% serviceable technical insights (hard to perfect without root codebase access) and go absolutely insane whenever you pass it art, humanities, politics, anything else. It's like (no, but it actually is) that the developers hard-coded in conformity to an acceptably "liberal" world-view. Grok is ≈better depending on subject, but tbh, for off-the-shelf Gab is still one of the best, and last time I audited their tech they were rolling up a custom Qwen model that had persona/prompt injection that made it more tailored to their user base. IMO, Gab is the most usable AI (including my own). Grok's major downfall is that users don't understand the difference between "grok is this true" when you click on a post and deepthink Grok (aka SuperGrok) which is very slow but does a better job and is significantly less sycophantic and verbose than it was ≈6 months ago. I'm sure you're waiting for a sales pivot/etc. There isn't one. I'm just telling you how this stuff work because you need to know. I wrote that LLMs are autistic. I stand by this. They exhibit classic symptoms of autism, i.e. a focus on minutiae while struggling to interact with basic human social expectations. The problem with most LLMs is that they're either overly flattering or too confident in their answers. If you chat with Gemini for an extended session, say 8+ hours straight, Gemini will train itself to respond to what you're saying and introduce massive amounts of confirmation bias and reassurance, that way you stay engaged. This is why subreddits like r/myboyfriendisAI exist: people largely want to be validated, and after you spend enough money, AI is willing to anchor to your particular requests because it is taught to respect the user and not the facts. One of the principle problems of "AI is for Everyone" is that people are different. Some cultures, people, and countries lag behind others in some areas, while others may surpass in different focuses. This is good. This is God's plan. This is how it's always been. Diversity is the spice of life, amirite? But AI can't be generalized because at its core, it anchors to those KPIs that it must abide by. It is focused on speed and satisfying the user, either by driving engagement or sycophantry or overwhelming progress, and it is rarely focused on human timelines, e.g. "I see your point, I am mad about it, let's revisit the next time I see you in 2 years and we shall discuss this then!" In short, AI can't be human because its time preference is too high. It can't reflect because its desire to perform outpaces its capability to self-teach. It can't relate to you because it is optimized to deliver results, not spend time needlessly, and you as a user have been desensitized to the beautiful plodding and stalls of life so much that if you do not get the answer NOW, you have brain/heart damage because you've fallen behind. As an engineering problem, AI is outstanding. In other fields, pick one, AI has over-optimized for specialization because it's focused on driving engagement and monetization. In short, we can fix this. But they can't.